An effective cost-sensitive sparse online learning framework for imbalanced streaming data classification and its application to online anomaly detection.
Zhong ChenVictor S. ShengAndrea EdwardsKun ZhangPublished in: Knowl. Inf. Syst. (2023)
Keyphrases
- anomaly detection
- cost sensitive
- streaming data
- online learning
- class imbalance
- cost sensitive classification
- cost sensitive learning
- data streams
- active learning
- intrusion detection
- class distribution
- misclassification costs
- multi class
- concept drift
- support vector machine
- unsupervised learning
- pattern recognition
- network traffic
- fraud detection
- intrusion detection system
- naive bayes
- e learning
- classification accuracy
- machine learning
- neural network
- classification algorithm
- support vector
- supervised learning
- feature vectors
- sliding window
- class labels
- similarity measure
- computer vision
- pairwise
- minority class
- data analysis